NLP Lesson 3 Quiz

What is the main purpose of transforming text into vectors in NLP?

• To represent text data in a mathematical form

• To count word frequencies

• To identify named entities

• To segment sentences

Which data structure is a one-dimensional array of numbers used in text data representation?

• Matrix

• Graph

• Vector

• Dataframe

What does the Bag of Words (BoW) method primarily focus on?

• Word sentiment

• Word order

• Word frequency in a document

• Named entity recognition

What is the limitation of the Bag of Words (BoW) model?

• It captures semantic meaning

• It ignores word order and context

• It uses machine learning models

• It reduces dimensionality

How does the Term Frequency-Inverse Document Frequency (TF-IDF) score a word?

• By counting the number of letters

• By combining term frequency and inverse document frequency

• By checking its position in the document

• By analyzing the syntax of the word

What does the Inverse Document Frequency (IDF) measure?

• How common or rare a word is in the entire corpus

• The frequency of a word in a document

• The length of a document

• The sentiment of a word

Which of the following is the primary limitation of One-Hot Encoding (OHE)?

• It needs to understand the semantic relationships between words

• It requires an excessively large vocabulary size

• It leads to high-dimensional sparse vectors

• It leads to ambiguous representations of words

Cosine similarity is used to measure the similarity between two vectors by calculating the:

• Euclidean distance

• Dot product and magnitudes

• Average of word frequencies

• Number of common terms

What does the "dot product" refer to in the context of cosine similarity?

• The total number of vectors

• The angle between two vectors

• The sum of the products of corresponding vector components

• The length of a vector

What is a key application of TF-IDF?

• Named entity recognition

• Search engine relevance ranking

• Part-of-speech tagging

• Sentence segmentation